Bhalla Deeksha, Ramachandran Anupama, Rangarajan Krithika, Dhanakshirur Rohan, Banerjee Subhashis, Arora Chetan
Department of Radiodiagnosis, Dr.BRA IRCH, All India Institute of Medical Sciences, New Delhi, India.
Department of Radiodiagnosis, Dr.BRA IRCH, All India Institute of Medical Sciences, New Delhi, India.
Curr Probl Diagn Radiol. 2023 Jan-Feb;52(1):47-55. doi: 10.1067/j.cpradiol.2022.04.003. Epub 2022 Apr 22.
With the rapid integration of artificial intelligence into medical practice, there has been an exponential increase in the number of scientific papers and industry players offering models designed for various tasks. Understanding these, however, is difficult for a radiologist in practice, given the core mathematical principles and complicated terminology involved. This review aims to elucidate the core mathematical concepts of both machine learning and deep learning models, explaining the various steps and common terminology in common layman language. Thus, by the end of this article, the reader should be able to understand the basics of how prediction models are built and trained, including challenges faced and how to avoid them. The reader would also be equipped to adequately evaluate various models, and take a decision on whether a model is likely to perform adequately in the real-world setting.
随着人工智能迅速融入医疗实践，提供用于各种任务的模型的科学论文数量和行业参与者呈指数级增长。然而，对于实际工作中的放射科医生来说，鉴于其中涉及的核心数学原理和复杂术语，理解这些内容很困难。本综述旨在阐明机器学习和深度学习模型的核心数学概念，用通俗易懂的语言解释各个步骤和常用术语。因此，在本文结尾，读者应该能够理解预测模型是如何构建和训练的基础知识，包括面临的挑战以及如何避免这些挑战。读者还将有能力充分评估各种模型，并决定一个模型在现实环境中是否可能表现良好。
